SUMmarizing antiMicrobial transmission data to Enable data Reanalysis and predictions by FAIR data use (SUMMERFAIR)
De impact van maatregelen tegen de verspreiding van antibioticumresistente bacteriën wordt berekend aan de hand van de hoeveelheid nieuw besmettingen (mens of dier). Er is veel data uit experimenten of het veld. De toegankelijkheid van deze data voor hergebruik is gelimiteerd. Hierdoor is deze niet herbruikbaar in nieuwe analyses en kan niet samengevoegd voor preciezere berekeningen. In het ergste geval worden experimenten of dataverzamelingen opnieuw gedaan.
Het SUMMERFAIR-project heeft twee hoofddoelen: Het toegankelijk maken van deze data door een soort algemene taal af te spreken èn algoritmes ontwikkelen voor het samenvoegen van verschillende databronnen voor betere en nieuwe schattingen van de verspreiding van antibioticumresistentie. Dit zal de evaluatie van maatregelen verbeteren.
Het project is een samenwerkingen tussen belangrijke wetenschappelijke expertises en met kennis vanuit de mens als het dier. Buitenlandse instituten worden nauw betrokken.
Auteur: Elena Slavco; Martine De Vos; Miel Hostens; Jan Top; Egil A.J. Fischer
Magazine: 2022 IEEE 18th International Conference on e-Science (e-Science)
Auteur: Slavco E., De Vos M , Hostens M , Top J , Bootsma M.C.J. , Hobbelen P. , Pacholewicz , Fischer E.A.J.
Auteur: Slavco E., De Vos M , Hostens M , Top J , Bootsma M.C.J. , Hobbelen P. , Pacholewicz E, Fischer E.A.J.
Samenvatting van de aanvraag
Clonal spread of antimicrobial resistant (AMR) bacteria in hospitals and in livestock flocks is the major driver for the spread of AMR even with prudent use of antimicrobials. Evaluation and development of Interventions targeted at the clonal spread of AMR require quantification of the transmission between patients and animals. Alone due to the immense number of AMR-gene and bacterium species combination, it is impossible to test all situations. Reuse and combination of existing data can improve estimation of transmission parameters and predict outcomes of yet unobserved situations. This will allow acceleration of innovation, increase precision of estimation, reduce experimental animals and enhance risk assessment of the spread of AMR in human and animal populations. Reuse of transmission data is hampered by a lack of standardizations of data storage and metadata, as well as a lack of algorithms to combine existing data sets. This project will target these two gaps in methodology. This project will define a domain ontology, which is a common language such that data can be accessed by generic algorithms. The ontology will be developed based on current informal standards, and presented and discussed through on-line consultation with national and international experts in transmission. Algorithms for combined data analyses will be developed. Local algorithm will produce aggregate estimates and metadata under local control (i.e. “within the walls of the institute” of the data owner) and report these to a global algorithm, which can use these local estimates to produce estimates based on combinations of the output of the local algorithm. The global algorithm will, thus, only be fed with by design anonymized aggregate information, which will prevent the need of transfer of confidential or privacy sensitive data. Two case studies will be conducted as proof-of-principle, but can already have impact on the spread of AMR in the Netherlands. Hospital data of IC units will be used to determine transmission dynamics of specific AMR pathogens, which have not yet been observed, and the effect of hygiene on the transmission will be investigated for AMR resistant and sensitive bacteria. From animal experimental data of the spread of AMR in poultry, a case study will try a more precise estimation of the contagiousness and susceptibility to colonization of chickens, as well as predicting the effect of interventions against the spread in different bacterial species and AMR genes, that are currently not yet tested. For these case studies, the project will take care that data are stored findable and accessible. The development of the ontology and algorithms will make these data also accessible, interoperable and reusable. A domain ontology for transmission data enables reuse and combination of data. Local and global algorithms allow data mapped to the ontology to be analyzed locally and combined globally. This will allow reuse of existing AMR transmission data to improve quantification of transmission in AMR, and predict interventions on transmission in yet unobserved situations. This will contribute to reduction of AMR by allowing assessment of intervention strategies with mathematical models, or by generalization of interventions to untested situations. Moreover this project will not only allow currently existing data to be used, but will enable combination with data from new studies, monitoring or experiments. For human clinical trials and animal experiments our methodology can save time and resources by allowing for better directed experimentation with smaller groups and by avoiding experiments with a high chance of failure. Directed trials and experiments will of course never become obsolete, but our project helps to increase the chances of success in new studies. Collaboration with international partners will be conducted by on-line consultation during the design of the domain ontology. Several parties have already responded positively with the intent to participate. The results of the project will be disseminated through contacts with these other centers of expertise and through education, as well as by scientific publications.